Hi everyone! We thought it would be handy to have all models combined in one file. We're currently making a pull request, but for now, you may refer to the code below if you want to use this as well 😄
''' Examples of data loading of moisture sources
Data is loaded per individual ensemble member for each model.
All data is converted to mm evaporative sources over the whole time period
WRF-WVT is not included yet, as well CHc Lagranto and univie FLEXPART
There might also be a few (new) ensemble members not included yet:
- For the HAMSTER model (Ughent) there is an additional ensemble member not included yet
- Extra ensemble members of Flexpart-Watersip produced by Fandy
'''
########################################################
## WRF-WVT ##
########################################################
# Just csv file because of nature of simulations #
########################################################
## WAM2layers ##
########################################################
basedir = "/user/gent/442/vsc44251/scratch/intercomparison/Pakistan/"
directory_str = basedir+"results WAM2layers/"
directory = os.fsencode(directory_str)
n=0
for file in os.listdir(directory):
filename = os.fsdecode(file)
if filename.endswith(".nc"):
if n ==0:
temp = xr.open_dataset(os.path.join(directory_str, filename))
a_gridcell, lx, ly = get_grid_info(temp)
srcs_wam2layers = temp["e_track"]* 1000 / a_gridcell[:, None]
n+=1
else:
temp = xr.open_dataset(os.path.join(directory_str, filename))
a_gridcell, lx, ly = get_grid_info(temp)
srcs_wam2layers += temp["e_track"]* 1000 / a_gridcell[:, None]
n+=1
continue
else:
continue
# Data loading this way gives an error message for me while plotting, but in principe it should work
dsall = xr.open_mfdataset(basedir+'results WAM2layers/backtrack_*T00-00.nc', combine = 'nested', concat_dim='time')
lat = dsall.latitude.values
lon = dsall.longitude.values
a_gridcell_new, l_ew_gridcell, l_mid_gridcell = get_grid_info_new(lat, lon)
E_track_totalmm = (dsall/ a_gridcell_new).rename(latitude='lat', longitude='lon') *1000 # mm
srcs_wam2layers_new = E_track_totalmm["e_track"].sum('time') # mm
########################################################
## University of Vigo ##
########################################################
srcs_Vigo_e1_Stohl = xr.open_dataset(basedir+"results Uvigo/ERA5_SJ05_reg.nc")["E_P"]
srcs_Vigo_e2_Sodemann = xr.open_dataset(basedir+"results Uvigo/ERA5_APA22_reg.nc")["E_P"]
########################################################
## UTRACK - Arie Staal ##
## - Based on the communication with Arie ##
## - the results should be area corrected ##
## - given the values I assumed that the area needs ##
## - to be in km**2, however this should be checked. ##
########################################################
directory_str = basedir+"results Utrack Arie Staal/moisture_tracking_intercomparsion/"
directory = os.fsencode(directory_str)
dsall = xr.open_mfdataset(basedir+'results Utrack Arie Staal/moisture_tracking_intercomparsion/*_mixing48h_dt025h_100p.nc', combine = 'nested', concat_dim='time')
a_gridcell_new, l_ew_gridcell, l_mid_gridcell = get_grid_info_new(dsall.lat.values, dsall.lon.values) #Calcluate grid cell area
srcs_utrack_e1 = dsall["moisture_source"].sum("time")*a_gridcell_new/10**6.0 #1000.0
dsall = xr.open_mfdataset(basedir+'results Utrack Arie Staal/moisture_tracking_intercomparsion/*_mixing24h_dt025h_100p.nc', combine = 'nested', concat_dim='time')
srcs_utrack_e2 = dsall["moisture_source"].sum("time")*a_gridcell_new/10**6.0 #1000.0
dsall = xr.open_mfdataset(basedir+'results Utrack Arie Staal/moisture_tracking_intercomparsion/*_mixing12h_dt025h_100p.nc', combine = 'nested', concat_dim='time')
srcs_utrack_e3 = dsall["moisture_source"].sum("time")*a_gridcell_new/10**6.0 #1000.0
dsall = xr.open_mfdataset(basedir+'results Utrack Arie Staal/moisture_tracking_intercomparsion/*_mixing24h_dt025h_1000p.nc', combine = 'nested', concat_dim='time')
srcs_utrack_e4 = dsall["moisture_source"].sum("time")*a_gridcell_new/10**6.0 #1000.0
dsall = xr.open_mfdataset(basedir+'results Utrack Arie Staal/moisture_tracking_intercomparsion/*_mixing24h_dt010h_100p.nc', combine = 'nested', concat_dim='time')
srcs_utrack_e5 = dsall["moisture_source"].sum("time")*a_gridcell_new/10**6.0 #1000.0
########################################################
## HAMSTER (Ghent) ##
########################################################
# E1: Sodemann #
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens1_sod08/bias_corrected_20220810120000_sod08.nc")
srcs_ghent_e1 = temp["E2P_BC"].mean("time") #Mean over time to remove time dimension
for date in range(11,25):
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens1_sod08/bias_corrected_202208" + str(date) + "120000_sod08.nc")
srcs_ghent_e1 += temp["E2P_BC"].mean("time")
# E2: FAS19 (Fremme + Sodemann, 2019) #
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens2_fas19/bias_corrected_20220810120000_fas19.nc")
srcs_ghent_e2 = temp["E2P_BC"].mean("time")
for date in range(11,25):
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens2_fas19/bias_corrected_202208" + str(date) + "120000_fas19.nc")
srcs_ghent_e2 += temp["E2P_BC"].mean("time")
# E3: RH20 () #
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens3_rh20/bias_corrected_20220810120000_rh20.nc")
srcs_ghent_e3 = temp["E2P_BC"].mean("time")
for date in range(11,25):
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens3_rh20/bias_corrected_202208" + str(date) + "120000_rh20.nc")
srcs_ghent_e3 += temp["E2P_BC"].mean("time")
# E4:
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens4_allabl/bias_corrected_20220810120000_allabl.nc")
srcs_ghent_e4 = temp["E2P_BC"].mean("time")
for date in range(11,25):
temp = xr.open_dataset(basedir+"results UGhent HAMSTER/Pakistan simulations/bias_corrected/ens4_allabl/bias_corrected_202208" + str(date) + "120000_allabl.nc")
srcs_ghent_e4 += temp["E2P_BC"].mean("time")
########################################################
## TRACMASS Dipanjan Dey ##
## Units in mm/day, so multiplied with # of ##
## event days ##
########################################################
nrdays = 15
ds_TRACMASS = xr.open_dataset(basedir+"results TRACMASS Dipanjan Dey/TRACMASS_diagnostics.nc") #Evaporative sources (and preicp?) mm/day
ds_pr_TRACMASS = xr.open_dataset(basedir+"results TRACMASS Dipanjan Dey/PR_ERA5_TRACMASS.nc") #Precip ERA5 and TRACMASS Comparison
#convert to -180 to 180 lon
ds_TRACMASS.coords['lon'] = (ds_TRACMASS.coords['lon'] + 180) % 360 - 180
ds_TRACMASS = ds_TRACMASS.sortby(ds_TRACMASS.lon)
srcs_TRACMASS = ds_TRACMASS["E_TRACMASS"]*nrdays #Units of data is mm/day but we want mm over whole time period
########################################################
## FLEXPART-Watersip TatFanCheng ##
########################################################
filename = basedir+"results FLEXPART_WaterSip_TatFanCheng/WaterSip_Cb_20220810-20220824_Pakistan_box.nc"
ds_flexpart_watersip = xr.open_dataset(filename)
#convert to -180 to 180 lon
ds_flexpart_watersip.coords['lon'] = (ds_flexpart_watersip.coords['lon'] + 180) % 360 - 180
ds_flexpart_watersip = ds_flexpart_watersip.sortby(ds_TRACMASS.lon)
srcs_flexpart_watersip = ds_flexpart_watersip.sum("time")["Cb"]
########################################################
## Flexpart Ru Xu ##
########################################################
ds_flexpart_xu = xr.open_dataset(basedir+"results Ru_Xu_FLEXPART/e_daily.nc").rename(longitude='lon', latitude='lat')
srcs_flexpart_xu = ds_flexpart_xu["variable"].sum("time")
########################################################
## Lagranto CHc ##
########################################################
ds_lagranto_CHc = xr.open_mfdataset(basedir+"results CHc LAGRANTO/Pakistan_2022_CHc_*.nc", combine = 'nested', concat_dim='time')
srcs_lagranto_CHc = ds_lagranto_CHc["N"].sum("time").squeeze().rename(dimy_N='lat', dimx_N='lon')
srcs_lagranto_CHc.assign_coords(lat=srcs_TRACMASS.lat, lon=srcs_TRACMASS.lon)
srcs_lagranto_CHc = srcs_lagranto_CHc.assign_coords(lat=srcs_TRACMASS.lat, lon=srcs_TRACMASS.lon)
########################################################
## FLEXPART UniVie ##
########################################################
ds_flexpart_univie = xr.open_dataset(basedir+"results univie FLEXPART/pakistan_univie.nc")
srcs_flexpart_univie = (ds_flexpart_univie["moisture_uptakes_bl"]+ds_flexpart_univie["moisture_uptakes_ft"]).sum('time')
# Combining data
datasets = {
'flexpart_univie': srcs_flexpart_univie,
'lagranto_CHc': srcs_lagranto_CHc,
'flexpart_xu': srcs_flexpart_xu,
'flexpart_watersip': srcs_flexpart_watersip,
'TRACMASS':srcs_TRACMASS,
'ghent_e4':srcs_ghent_e4,
'ghent_e3':srcs_ghent_e3,
'ghent_e2':srcs_ghent_e2,
'ghent_e1':srcs_ghent_e1,
'utrack_e5':srcs_utrack_e5,
'utrack_e4':srcs_utrack_e4,
'utrack_e3':srcs_utrack_e3,
'utrack_e2':srcs_utrack_e2,
'utrack_e1':srcs_utrack_e1,
'Vigo_e2_Sodemann':srcs_Vigo_e2_Sodemann,
'Vigo_e1_Stohl':srcs_Vigo_e1_Stohl,
'wam2layers_new':srcs_wam2layers_new,
}
all_maps = xr.Dataset(datasets)
all_maps.to_netcdf("combined_data.nc")
Peter9192
commented
5 months ago
Hi everyone! We thought it would be handy to have all models combined in one file. We're currently making a pull request, but for now, you may refer to the code below if you want to use this as well 😄